Passive seismic event estimation using multi-scattering waveform inversion

Chao Song, Zedong Wu, Tariq Ali Alkhalifah

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


Passive seismic monitoring has become an effective method to understand underground processes. Time-reversal-based methods are often used to locate passive seismic events directly. However, these kinds of methods are strongly dependent on the accuracy of the velocity model. Full waveform inversion (FWI) has been employed on passive seismic data to invert the velocity model and source image, simultaneously. However, waveform inversion of passive seismic data utilizes mainly the transmission energy, which results in poor illumination and low resolution. We develop a waveform inversion using multi-scattered energy for passive seismic to extract more information from the data than conventional FWI. Using transmission wavepath information from single- and double-scattering, computed from a predicted scatterer field acting as secondary sources, the proposed method provides better illumination of the velocity model than conventional FWI. Using a new objective function, we optimize the source image and velocity model including multi-scattered energy, simultaneously. As we conduct our method in the frequency domain with a complex source function including both spatial and wavelet information, we mitigate the uncertainties of the source wavelet and source origin time. Inversion results from the Marmousi model show that by taking advantage of multi-scattered energy and starting from a reasonably acceptable frequency (single source at 3 Hz and multiple sources at 5 Hz), the proposed method yields better inverted velocity models and source images compared with the conventional FWI.
Original languageEnglish (US)
Pages (from-to)KS59-KS69
Number of pages1
Issue number3
StatePublished - Mar 11 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We thank KAUST for its support and the SWAG group for the collaborative environment. This work utilized the resources of the Supercomputing Laboratory at King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia, and we are grateful for that.


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